Event-Triggered Replanning Mechanisms

Updated 8 December 2025

Event-triggered replanning mechanisms are strategies that update control plans upon detecting significant events, such as obstacles or trajectory deviations.
These methods employ fast, almost-surely asymptotically optimal planners, distributed DMPC, and hierarchical architectures to enhance resource efficiency and response reliability.
Empirical studies demonstrate superior success rates and reduced median path lengths compared to traditional time-triggered or incremental planning approaches.

Event-triggered replanning mechanisms constitute a class of control and planning strategies in which updates to motion plans, control sequences, or collaborative policies are performed in response to detected events rather than on a fixed schedule. These mechanisms are increasingly prevalent in robotics, autonomous systems, networked control, and multi-agent coordination, enabling adaptive, resource-efficient, and high-reliability operation in dynamic or uncertain environments (Sabbadini et al., 24 Oct 2025, Gräfe et al., 2022, Borate et al., 27 Nov 2025, Wildhagen et al., 2021).

1. Fundamental Principles and Definitions

Event-triggered replanning is characterized by the explicit coupling of plan or control updates to environment- or system-state-driven events. In robotic motion planning, an event typically corresponds to the detection of a new obstacle within the robot's sensing horizon, significant deviation from a predicted trajectory, failure of a subtask, or explicit intent change from a human user (Sabbadini et al., 24 Oct 2025, Borate et al., 27 Nov 2025).

Let $X \subseteq \mathbb{R}^n$ denote the system's state space, $X_{\mathrm{obs}}(t)$ the time-varying obstacle set, and $B(r_{\mathrm{s}}, x)$ a sensor footprint of radius $r_{\mathrm{s}}$ . The currently sensed obstacles are

$X_{\mathrm{sensed}}(t) = X_{\mathrm{obs}}(t) \cap B(r_{\mathrm{s}}, x(t))$

and a planning event is triggered whenever $X_{\mathrm{sensed}}(t) \setminus X_{\mathrm{sensed},i-1} \neq \emptyset$ or the system reaches the boundary of its known free space (Sabbadini et al., 24 Oct 2025).

In networked and distributed systems, events may include hard threshold crossings on state estimates, constraint violations, communication errors, or collaborative task failures (Gräfe et al., 2022, Borate et al., 27 Nov 2025, Wildhagen et al., 2021). Event detection enables the system to avoid unnecessary recomputation or data transmission during nominal operation, reducing resource consumption while guaranteeing responsiveness to non-nominal conditions.

2. Algorithmic Architectures

Event-triggered replanning mechanisms display highly diverse architectures, dependent on application domain, from single-robot planning in dynamic fields to distributed control of UAV swarms and hierarchical multi-robot systems.

2.1 Incremental Replanning via Fast ASAO Planners

Motion planning under event-triggered regimes is effectively addressed via single-query, almost-surely asymptotically optimal (ASAO) algorithms. For example, Effort Informed Trees (EIT*) and Asymptotically Optimal RRT-Connect (AORRTC) discard prior plans at each event and solve a new optimal planning problem conditioned on the newly sensed environment (Sabbadini et al., 24 Oct 2025). The core pseudocode is:

\begin{algorithm}[H]
\KwIn{State space %%%%5%%%%, start %%%%6%%%%, goal %%%%7%%%%, sensor radius %%%%8%%%%, time‐budget %%%%9%%%%, planner %%%%10%%%%}
\While{%%%%11%%%%}{
  Define %%%%12%%%%;
  %%%%13%%%%;
  Execute %%%%14%%%% to boundary or event;
  Sense new obstacles; %%%%15%%%%
}
\end{algorithm}

2.2 Distributed Event-Triggered Collaborative Planning

Distributed Model Predictive Control (DMPC) can incorporate event-triggered replanning, selectively updating only a subset of agent trajectories (as decided by a priority function) in each round. In UAV swarms, the Priority-Based Trigger (PBT) scores each agent and triggers replanning for those with the highest priorities, subject to network and computational constraints (Gräfe et al., 2022).

2.3 Hierarchical and Human-Aware Replanning

Event-driven architectures such as CoMuRoS employ centralized deliberation (Task Manager LLM) and decentralized robotic execution. Local events, classified via embedded vision-LLMs or RL policies, are filtered and reported to a central manager, where a formal logical predicate defines the replanning trigger: $R \Leftrightarrow \Bigl( \bigvee_{e\in E_{\rm raw}: s(e) \geq \tau_{\rm relev}} \Bigr)\; \vee\; \Delta_{\rm user} \; \vee\; \bigl( \exists j: S_T[T_j]=\mathrm{INTERRUPTED}\bigr)$ (Borate et al., 27 Nov 2025).

2.4 Rollout Event-Triggered Control

In networked linear systems, rollout event-triggered control (ETC) embeds both plant and traffic constraints in a receding-horizon OCP. At each step, the controller "replans" the schedule and control input, transmitting only if an event-optimized policy justifies it, subject to bandwidth/token bucket constraints (Wildhagen et al., 2021).

3. Comparison With Time-Triggered and Incremental Approaches

Event-triggered replanning diverges sharply from purely time-triggered schemes, which operate at preassigned periodic intervals regardless of actual system need. Classical incremental planners—such as D*, LPA*, and RRT^X—maintain and repair a graph in response to sensed changes, requiring bookkeeping to identify affected areas.

Empirical investigations demonstrate that fast ASAO planners such as EIT* and AORRTC, when used in an event-triggered, full-replan-from-scratch regime, can (i) outperform incremental repair approaches in both success rate and median solution quality, and (ii) obviate the need for detailed change-location maps. As shown in Table 1, EIT* can achieve a 100% success rate and considerably shorter median path lengths than reactive repair-based methods in multiple 2D environments (Sabbadini et al., 24 Oct 2025).

World	RRT-C	RRT-C (sm)	RRT*	RRT^X	RRT^X(init)	EIT* (ASAO)
Random Rect.	91%	94%	9%	1%	3%	100%
Wall Gap	100%	100%	79%	12%	10%	100%
Double Encl.	88%	87%	0%	0%	0%	100%

Theoretical properties of rollout ETC further guarantee strict compliance with network bandwidth requirements, in contrast to ETC which exhibits unpredictable traffic (Wildhagen et al., 2021).

4. Performance Guarantees and Empirical Metrics

Event-triggered replanning mechanisms offer a spectrum of formal and practical guarantees, including:

Almost-Sure Asymptotic Optimality: EIT* and AORRTC converge to the globally optimal solution with probability one as the number of samples increases, under mild regularity conditions (Sabbadini et al., 24 Oct 2025).
Recursive Feasibility and Collision Avoidance: In UAV swarms, event-triggered DMPC maintains feasible, collision-free solutions via time-variant buffered Voronoi constraints, even as only a subset of agents replan per round (Gräfe et al., 2022).
Resource Efficiency: In swarms, triggering updates for only a subset of agents saves ~60% of network and computational resources (for M ≪ N), while PBT outperforms simple round-robin selection in success rates and time-to-target (Gräfe et al., 2022).
Correctness and Task Recovery in Hierarchical Systems: CoMuRoS achieves high task allocation, classification, and executability (TA=0.96, TC=0.96, Executability=0.98) and 1.0 correctness in replanning, explicitly demonstrating hardware-validated recovery from disruptive events in multi-robot settings (Borate et al., 27 Nov 2025).
Bandwidth Compliance with Performance Bounds: Rollout ETC achieves strict adherence to traffic specifications while improving control cost metrics relative to TTC; theorem-backed performance guarantees ensure asymptotic stabilization and efficiency (Wildhagen et al., 2021).

5. Implementation Guidelines and Parameterization

Implementation of event-triggered replanning requires domain-specific tuning and architectural choices. For robotic motion planning, critical guidelines include:

Sensing and Event Detection: Continuous sensor fusion, local map updates, and difference detection are used to fire callback triggers. The planner time budget $T$ should not exceed control cycle durations (typically 20–50 ms for manipulation, 50–100 ms for navigation) (Sabbadini et al., 24 Oct 2025).
ASAO Planner Parameterization: For EIT*, use batch sizes of 50–200, rewiring radius factors of 1.0–1.1, and early termination when no improvement is seen in the final 10% of $T$ . For AORRTC, set step size to 0.2–0.4 workspace diameter, and use RRT* connection radius $\gamma\left( \frac{\log n}{n} \right)^{1/d}$ (Sabbadini et al., 24 Oct 2025).
Heuristics for Bias: Incorporate 5–10% goal bias and seed points at frontier of sensed obstacles.
Event Filtering in Hierarchical Teams: Use confidence thresholds (e.g., $\tau_{\rm relev} = 0.7$ ) for event reporting to a centralized task manager, reducing false triggers (Borate et al., 27 Nov 2025).
Priority Functions in Swarms: Employ composite priority metrics accounting for task urgency, last replanned round, and positional relationships (e.g., blocking agents), distributing replanning load fairly (Gräfe et al., 2022).

6. Limitations and Ongoing Research Directions

Event-triggered replanning mechanisms, while efficient and robust, exhibit several limitations:

Partial Progress and Graceful Degradation: Current frameworks rarely reason about partial task fulfillment or “safe-abort”; full-recovery is often attempted (Borate et al., 27 Nov 2025).
Parameter Sensitivity and Tuning: Thresholds for event relevance, trigger frequency, and batch sizes require careful calibration and are commonly set offline (Sabbadini et al., 24 Oct 2025, Borate et al., 27 Nov 2025, Gräfe et al., 2022).
Long-Horizon Context Bottlenecks: Token usage may grow linearly with execution history, motivating research into summarization and memory management for prompt-based systems (Borate et al., 27 Nov 2025).
Scalability in High-Frequency Domains: For high-rate event regimes (<10 ms), even fast ASAO planners may reach computational limits unless highly optimized (Sabbadini et al., 24 Oct 2025).

Research is ongoing into adaptive thresholding for event relevance, integration of shared spatial maps in collaborative teams, and the design of prompt-pruning or retrieval-augmented mechanisms for context-efficient hierarchical planners (Borate et al., 27 Nov 2025). Rollout ETC schemes are further developed for guaranteed performance under complex network traffic specifications (Wildhagen et al., 2021).

7. Representative Applications

Mobile Manipulation in Dynamic Environments: Event-triggered replanning with EIT* and AORRTC achieves real-time response to moving obstacles with high path quality and success rates (Sabbadini et al., 24 Oct 2025).
Swarm UAV Coordination: ET-DMPC with priority-based triggers supports collision-free, resource-efficient trajectory management in swarms of up to 25 UAVs, with proven safety guarantees and hardware-in-the-loop results (Gräfe et al., 2022).
Heterogeneous Team Task Execution: Hierarchical LLM-driven event-driven planners enable autonomous recovery and reallocation in multi-robot and human-robot collaboration tasks, verified in both simulation and physical deployment (Borate et al., 27 Nov 2025).
Networked Control with Bandwidth Constraints: Rollout ETC unifies state-driven event triggers and guaranteed bandwidth compliance in networked linear plants, achieving performance improvements over purely periodic policies (Wildhagen et al., 2021).

These applications illustrate the growing impact of event-triggered replanning as a rigorously founded, high-performance solution for complex, dynamic, and resource-constrained control and planning challenges across robotics and autonomous systems.